Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their advantages and disadvantages. As there is currently no consensus on testing methods, it is quite challenging to compare the performance between algorithms. In this paper, we propose AvoidBench, a benchmarking suite which can evaluate the performance of vision-based obstacle avoidance algorithms by subjecting them to a series of tasks. Thanks to the high fidelity of multi-rotors dynamics from RotorS and virtual scenes of Unity3D, AvoidBench can realize realistic simulated flight experiments. Compared to current drone simulators, we propose and implement both performance and environment metrics to reveal the suitability of obstacle avoidance algorithms for environments of different complexity. To illustrate AvoidBench's usage, we compare three algorithms: Ego-planner, MBPlanner, and Agile-autonomy. The trends observed are validated with real-world obstacle avoidance experiments.

Existing multirotor-based cargo transportation does not maintain a constant cargo attitude due to underactuation; however, fragile payloads may require a consistent posture. The conventional method is also cumbersome when loading cargo, and the size of the cargo to be loaded is limited. To overcome these issues, we propose a new fully-actuated multirotor unmanned aerial vehicle platform capable of translational motion while maintaining a constant attitude. Our newly developed platform has a cubic exterior and can freely place cargo at any point on the flat top surface. However, the center-of-mass (CoM) position changes when cargo is loaded, leading to undesired attitudinal motion due to unwanted torque generation. To address this problem, we introduce a new model-free center-of-mass position estimation method inspired by the extremum-seeking control (ESC) technique. Experimental results are presented to validate the performance of the proposed estimation method, effectively estimating the CoM position and showing satisfactory constant-attitude flight performance.

Terrorist gangs smuggling Iranian missiles and drugs in the Middle East will soon have a fleet of 100 robo-ships hunting them down, Britain's most senior military commander in the Gulf has revealed. The new naval force, made up of drones that can operate around the clock, will be up and running by the summer, claimed Commodore Adrian Fryer. It comes as the top officer insisted he remained'alive' to the ever-present threat posed by Iran after a series of recent stand-offs, with the Commodore calling the Middle Eastern state the'main destabilising country' in the region. A small number of the unmanned vessels currently operating in the Gulf are already proving their worth, gathering invaluable data and information that's helping Royal Navy warships cut off smuggling routes and ambush gangs at sea. During the past 12 months, frigate HMS Montrose has seized her biggest haul of narcotics in its three years in the Gulf, bagging a whopping £46.8 million, bringing the ship's total during its time in the Middle East to a staggering £111.1 million.

India's agricultural sector has usually not kept pace with new technology, relying largely on traditional methods. But start-ups specialising in drone technology are trying to change that - one farm at a time. These companies are encouraging farmers to use drones to monitor the health of crops and spray them with fertiliser and pesticides. The trend comes amid the federal government's ambitious plan to make India a hub for drones by 2030. The BBC's Arunoday Mukharji travelled to Kancheepuram district in the southern state of Tamil Nadu to understand how farmers are adapting to working with the device on their fields.

Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.

Drone video of a deadly standoff that took the life of a Riverside County sheriff's deputy on Friday appears to capture the suspect being shot by another deputy on the street before the wounded officer is rushed into the back of a sheriff's SUV. The video was posted to YouTube late Friday by a brand new account created the same day. Its owner could not immediately be reached for comment by The Times on Saturday. The video provided the clearest picture yet of the encounter, which took the life of Deputy Darnell Calhoun, 30. The sheriff's department said Calhoun was fatally shot after responding to an "unknown trouble" call about 4:20 p.m. in the 18500 block of Hilldale Lane, a residential area in Lakeland Village.

Drones have become increasingly popular in a variety of fields, including agriculture, emergency response, and package delivery. However, most drone operations are currently limited to within Visual Line of Sight (vlos) due to safety concerns. Flying drones Beyond Visual Line of Sight (bvlos) presents new challenges and opportunities, but also requires new technologies and regulatory frameworks, not yet implemented, to ensure that the drone is constantly under the control of a remote operator. In this preliminary study, we assume to remotely control the drone using the available ground cellular network infrastructure. We propose to plan bvlos drone operations using a novel multi-layer framework that includes many layers of constraints that closely resemble real-world scenarios and challenges. These layers include information such as the potential ground risk in the event of a drone failure, the available ground cellular network infrastructure, and the presence of ground obstacles. From the multi-layer framework, a graph is constructed whose edges are weighted with a dependability score that takes into account the information of the multi-layer framework. Then, the planning of bvlos drone missions is equivalent to solving the Maximum Path Dependability Problem on the constructed graph, which turns out to be solvable by applying Dijkstra's algorithm.

A spherical robot has many practical advantages as the entire electronics are protected within a hull and can be carried easily by any Unmanned Aerial Vehicle (UAV). However, its use is limited due to finding mounts for sensors. Pendulum actuated spherical robot provides space for mounting sensors at the yoke. We study the non-linear dynamics of a pendulum-actuated spherical robot to analyze the dynamics of internal assembly (yoke) for mounting sensors. For such robots, we provide a coupled dynamic model that takes care of the relationship between forward and sideways motion. We further demonstrate the effects of wobbling and precession captured by our model when the bot is controlled to execute a turning maneuver while moving with a moderate forward velocity, a practical situation encountered by spherical robots moving in an indoor setting. A simulation setup based on the developed model provides visualization of the spherical robot motion.

Deployed on a scale never seen before to carry out both surveillance and strikes, drones ranging from small commercially-available models to larger aircraft have become a defining feature of the Ukraine conflict. Drones have been a part of warfare for years, employed extensively by the United States during the "War on Terror," and they have played important roles in conflicts including in Iraq and in the Nagorno-Karabakh region. But the degree to which they are being used by both sides in Ukraine -- and the benefits they bring, as well as the threats they pose -- highlights the importance for militaries to be ready to employ and to counter drones in future conflicts. "The size and the scale of drone use in Ukraine supersedes all the previous conflicts," said Samuel Bendett, a researcher in uncrewed military systems who is an analyst with the CNA Russia Studies Program. Bendett stressed the "absolutely unprecedented use of commercial-type drones" for both surveillance and combat in Ukraine, and said the war has shown that "small... tactical drones are absolutely essential -- at every unit, every platoon level, every company level."

This work introduces TRIGGER, the first lighTweight univeRsal jammInG Gripper for aErial gRasping. TRIGGER is an omnidirectional, landing-capable aerial grasping system with resilience and robustness to collisions and inherent passive compliance. In particular, this work presents the design, fabrication, and experimental validation of a novel, intelligent, modular, universal jamming gripper specifically designed for aerial applications. Leveraging recent developments in particle jamming and soft granular materials, TRIGGER produces 15N of holding force with only a relatively small activation force of 2.5N. Experiments show the relationship between fill ratio and activation force and reveal that adding an additive to the membrane's silicone mixture improves the holding force by up to 52%. The concept is validated by mounting TRIGGER onto a multicopter performing a pick-and-release task under laboratory conditions. Based on the experimental data, a model for robotic simulators is introduced to facilitate future controller developments.